Tag Archives: Business Success

Feb

Here are the top analytics trends 2017 for businesses based on what industry and our clients are saying.

These trends reveal a pattern similar to the one observed last year. Embedded BI facilitates the analytics of everything on demand. Moreover, application of IoT devices continues to increase rapidly. Gartner estimates that 20.8 billion connected things will be in use worldwide by 2020.

While analytics, IoT and their applications in business continue to permeate deeper, artificial intelligence (AI) and machine learning (ML) is gaining further attention.

Until a few years back, mid-size organizations hardly considered AI as a possible solution to any of their problems. However, the pressure on margins due to increasing competitiveness fueled by online players is making it imperative for all businesses, big and small, to be more efficient.

Besides analytics, IOT, and AI, there is one interesting trend that silently continues to grow and intensify because of how human beings are evolving – the urgent need for clear, relevant, and crisp visualization of data.

According to a research by scientists, human attention span is shrinking so much that even a goldfish can hold a thought for longer. The study by Microsoft says that average human attention span has fallen from 12 seconds in 2000, (or around the time the mobile revolution began), to 8.25 seconds in 2015.

While the comparison with the attention span of goldfish is debatable, the underlying insight – that humans are less attentive than ever before – hardly is. Powerful visualization of information remains the key.

Another trend catching the attention of businesses is the use of predictive analytics. In today’s uncertain business environment, companies want the ability to forecast future business performance based on the past. Predictive analytics tries to answer questions such as: What is likely to happen tomorrow? How can we make the business improve? Consequently, predictive and prescriptive analytics are among the most discussed analytics trends among the professionals.

In summary, smart businesses are recognizing the contribution of analytics (and the associated technologies) in their ongoing success. The top analytics trends 2017 continue to reflect this new reality. Unfortunately, Business analytics talent is scarce. Companies are struggling to hire (and afford) the right people that will help them realize the true benefits of analytics. This makes it ever-more critical to engage with partners that will bring on-board the right combination of computing know-how, analytical and visualization skills, and business acumen.

So, here are the top analytics trends 2017 at a glance. Do read-on, review and respond.

Jan

It’s New Year again – Happy New Year 2016!

Thanks for your overwhelming response to our insights shared with you over the last year. We are excited to announce the most popular perspectives from 2015 published at Veravizion/Perspectives. These are our biggest stories of 2015 in case you missed them.

One of the wonderful aspects about sharing our insights is appreciating the incredible business acumen, diversity, and depth of thinking of our readers. Our articles, which we call our perspectives, are written after carrying out thorough research on every topic. Our belief is that these articles will push you into thinking about how the (business) world is transforming before our eyes, and how some long-standing business principles may not necessarily hold true today.

As the year is over, take a quick glance at how the world is getting used to being data-driven. Enjoy these stories and let us know about your top content in the comments. In the next one, we will see how the analytics world is likely to unfold in 2016.

This article on Data Science by Veravizion was originally published as the cover story in the July-2015 edition of Computer Society of India – Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July)).“

Historically, leaders of cities, communities, and organizations have been embracing strategic initiatives to ensure long term sustenance and growth of their respective ecosystems. Many a times, these initiatives were ‘intentionally’ directed at bringing about long term transformation of their systems. But do such initiatives specifically aimed at strategic transformation always result in the lasting growth of the entity? We discuss it in this article.

This is the last article in the Digital Business series in which we illustrate how small and medium businesses can transform themselves from mere-physical to also-digital, and be more competitive. We do this by taking a visual example of a fictitious light business of our lovable businessman Bobstick.

We hope you enjoy these stories!

Strategic transformation photo credit: businessinsider

You can also subscribe to our blog –Our Perspectives– to receive interesting articles and insights in email. We would love to read your perspectives and comments on that.

Jul

“This article reposted here was originally published as the cover story in the July-2015 edition of Computer Society of India - Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July))."

Data Science means extraction of knowledge from data. The key word in data science is not data; it is science[1]. Science of something means study of that thing to extract knowledge about it. In most generic sense, the purpose of every data science project is to answer a question (or a set of questions) backed by hard-facts. Academicians and researchers apply scientific principles to get specific answers about a research subject. Similarly, businesses employ data science principles to improve customer engagement, devise growth strategies, optimize operations, and build competitive advantage. This article shares a perspective on what data science really is, how it impacts various industries, what benefits does it offer to organizations – both for-profit and not-for-profit, and what are the key data science trends prevalent today.

DATA SCIENCE: WHAT IT IS (AND ISN’T)

Apparently Peter Naur and John W. Tukey seem to be among the first ones to have treated data analysis within the precincts of science[2]. John W. Tukey, who coined the term ‘bit’, has mentioned it in his 1962 paper ‘The Future of Data Analysis’. In my view, while the term ‘data science’ is relatively young, its application is not. There is an early evidence[3] of 1854, of Dr. John Snow applying scientific principles of data analysis to detect the root cause of The Cholera Epidemic in London. So data science has been around for a while albeit in different forms.

While we tend to associate data science with several other terms such as artificial intelligence, machine learning, data mining, analytics, statistics, computer science, and operations research, each has its own specific meaning that is different from another. Artificial intelligence is intelligence exhibited by machines and it pertains to the creation of a software system that simulates human intelligence. Machine learning is a science that involves development of self-learning algorithms which can be used to make data-driven predictions in a similar but unfamiliar environment. Popular examples include self-driving cars and web searches. Statistics is a study of collection, organization, analysis, and interpretation of numerical information from data. Data mining is the practice of analyzing data using (machine-learning) algorithms and statistical techniques in order to solve a problem. Computer science covers computational complexity, distributed architectures such as Hadoop, data compression, optimization of data flows, and not to mention computer programming languages (such as R, Python, and Perl). Advanced analytics or Analytics is just a marketing driven terminology that applies many of the data science principles to solve complex problems faced by businesses and society. So while the differences are subtle, each one has its own application in industry and academia. Nevertheless, data science overlaps with computer science, statistics, operations research, and business intelligence in many ways and almost completely encompasses data mining and machine learning.

The subtle differences notwithstanding, data science is an independent discipline which amalgamates statistics, computing skills, and domain knowledge. At the core, data science helps in deriving valuable insights from data. The data science process involves data collection, data pre-processing and cleaning, data modelling and analysis, and insights generation which are applied within a given functional domain to make decisions. Although the process is similar to knowledge discovery and data mining (KDD), a data scientist requires computing skills and domain knowhow to arrive at context-specific decisions. The person working in data science needs to exhibit three distinct skills applied in the different phases of a data science project. As shown in EXHIBIT-A[4], an individual with data science expertise possesses (or needs to possess) a combination of mathematics and statistics knowledge, hacking skills, and substantial domain understanding. The hacking skills include familiarity (but not necessarily proficiency) with software programming but more importantly, a propensity at being able to manipulate any type of data. This is because real-world data hardly exists in a nice tabular format. It[5] is scattered in thousands of text files or on hundreds of web sites or in numerous unstructured excel sheets at best. True data scientists that possess all the three skills are not abundant; because the role entails making sense of amorphous data, deriving bespoke models, and developing algorithms to analyse a complex problem specific within a domain.

Unfortunately, simply churning out numbers or fiddling with inefficient models rarely solves a problem. This is the reason data scientist is one of the most coveted roles in industry today.

Data science is being applied in many industries. Some of the uses in various industries include weather forecasting, intuitive search in online search technology, customer engagement in retail and consumer products and services, fraud detection in banking and credit cards, prediction of sources of energy in Oil and Gas, evidence based medicines in healthcare, and sentiment analysis from social network feeds. Some fields that are routinely implementing analytics services are eCommerce, retail, consumer products and services, financial services, insurance, pharmaceuticals, manufacturing, telecommunications, and high-tech.

HUNTING PEARLY INSIGHTS IN THE OCEAN OF DATA WITH DATA SCIENCE

More and more businesses are embracing data science and analytics in multiple organizational functions. There are mainly three most common ways in which data science is deployed depending on the size of an organization. Large corporations usually deploy their own in-house analytics departments by recruiting data analysts. Business leaders in large corporations typically have humongous quantities of data to sift through in order to make decisions that are important for their business growth. While having an in-house analytics team may not always be an ideal way for institutionalizing data science, even for large corporations, they seem to be driven by large amount of resources at their disposal. Secondly, some companies prefer to buy a COTS (Commercial-Off-The-Shelf) product to cater to some standard requirement. Thirdly, many mid-to-large sized companies prefer to employ customized data science or analytics services to solve their specific data analysis and business operational requirement. This option seems ideal for businesses looking for the flexibility to hire precise services for their bespoke needs.

While the data science projects in most for-profit organizations are getting more and more complex, the fundamental purpose underlying these projects remain the same – to achieve sustainable growth and improve profitability for their businesses. To that effect, the companies put data science into action to gain meaningful insights into their customers, operational processes, supply chain and logistics, product and/or service usage, financial aspects, and future business performance. Conventionally, data science has mostly been applied for market research and market segmentation. However, businesses have a lot more at stake with every business decision as competition has become more and more intense. Gone are the days when business decisions used to be taken on gut-feeling. In today’s globalized world, every major business decision needs to be data-driven. Data science assists organizations and individuals in making fact-based decisions that they can take and defend confidently. That is why it has become essential for organizations, business or otherwise, to deploy data science projects in every division responsible for making any kind of decisions. Some of the types of data science and analytics projects include customer focused analytics through clustering, recommendation engines, root cause analysis, automated rule engines, conjoint analysis to quantify perceived value of features offered, process simulations for operational analysis, predictive modeling for business forecasting, and clustering analysis to identify anomalies, just to name a few.

BENEFITS FROM IMPLEMENTING DATA SCIENCE INITIATIVES

There are some fantastic examples of business organizations gaining huge benefits by systematically and strategically deploying analytics initiatives that involve data science and ethnographic research. Procter & Gamble has institutionalized the data and design thinking approach to such as extent that it is now ingrained into their DNA. The result is that P&G boasts of more than 20 billion-dollar brands in their product kitty. Amazon, a technology company and not just an eRetailer, is really surviving and thriving by understanding customer preferences through the implementation of numerous algorithms. It has helped them to grow quickly from selling just books online in 1996 to target-selling twenty million products in countless other categories. There are many examples of smaller companies that streamlined their processes and implemented analytics based strategies to grow and enter into the big league. Data science initiatives within companies have rendered meaningful insights to drive their firm’s customer experience. These companies have utilized the insights to define their business growth strategies and pursue a culture of data-driven decision making. The benefits include getting pointers to new growth areas, generating ideas to introduce innovative new products, decreasing cost bases and improving productivity to boost profitability, identifying risks of obsolete technologies in their processes, detecting bottlenecks in supply chain, and streamlining inefficient operations.

Even as data science is rapidly changing the business world, it is also spreading its influence on other sectors such as academic research, governments, and social organizations. While the data deluge has increased the complexity for these sectors to analyze the data in a timely manner, it has also opened a plethora of opportunities for them.

Academic institutions in regions such as US, UK, and some countries in Asia are facing sustainability issues due to severe cuts in funding and grants. They are able to apply data science within their own institutional spheres to identify their respective competitive advantage and attract the right students to strengthen their reputation further. Similarly, medical research institutions are now able to work on projects like genome research, DNA sequencing, and stem-cell research for treatment of fatal diseases such as cancer and AIDS. Economists are able to analyze the publicly available data to determine relationships between income levels, education, health, and quality of life.

Governments and public sector organizations are concerned about issues such as monitoring and prevention of terrorist activities, early-detection and control of pandemics, and uniform aid distribution among the poorer countries, which they are able to tackle by sponsoring appropriate data science initiatives.

TACKLING CHALLENGES ALONG THE WAY

Data privacy and security concern has been one of the main reasons keeping some businesses from adopting data science. Moreover companies are facing real challenges in terms of bad quality of data, data inconsistencies, unreliable third party data, and information security. Nonetheless, all roads to meaningful business insights lead through data, whether it is organizational or public. Businesses need to put in place appropriate mechanisms to share data in a controlled manner with analysts and service providers in order to generate hidden insights that can be utilized for business benefits. Data breaches and data thefts remain a valid concern too. Past incidents, albeit few and sporadic, of customer confidential information getting stolen have deterred some from initiating analytics projects. However, business organizations are coming around to the fact that they are fast losing their competitive advantage to rivals due to staying away from analytics. Increasing number of organizations is taking up analytics to secure and grow their businesses as they do not want to be left behind any more. Organizations will increasingly recognize that it is not possible to operate in a 100 percent secured environment. Once organizations acknowledge that, they can begin to apply more-sophisticated risk assessment and mitigation tools. They will look to embed security at multiple levels viz. application-level, execution-level, storage-level, and even contract level. Interestingly, analytics itself is proving to be a great mechanism for security breach prevention.

KEY TRENDS AND THE ROAD AHEAD

In some of the western countries, data science has been thoroughly internalized within large corporations. Even the smaller businesses there employ analytics services to achieve specific business objectives. In India, while the (few) big corporations seem to be deploying such initiatives, most other organizations are still in the nascent stage. One survey of SME business owners cited that most common reasons for the slow pace of embracing [data science] are lack of awareness about the value offered by analytics, dearth of skilled resources, apprehension about technological complexity, cost and ROI concerns, and data security risks.

Notwithstanding the current adoption level, businesses are realizing that they may be taking a big risk not considering data science and analytics as a potent competitive strategy. There is a tremendous rise of personal data originating from social-media, sensor-originated data from wearables, and the Internet of Things (IoT) with the recent surge in the use of smartphones. More and more human actions are generating Exabytes of data today. To get a sense of the amount of data being generated, let’s just say that we will need around 50 billion trees made into paper to print 1 Exabyte of data. That’s roughly 9 huge stacks of papers, each touching Mars from Earth. This enormous amount of data will be of no use if not analyzed and utilized appropriately.

These trends are pushing businesses to re-think their business and growth strategies. There is an increased focus on teaching data science based courses by colleges and universities worldwide. Companies are realizing that the business environment has become uncertain with the fast pace of technological and demographical changes. As a result, many organizations are allocating higher budgets for deploying customized analytics for their businesses to deepen customer understanding, engage customers through multiple channels, identify new sources of revenue, improve productivity and profitability, streamline business processes, and build competitive advantage. Going forward, use of customized analytics will become pervasive. More and more organizations will develop their unique value propositions around the valuable insights they gain about their existing and prospective customers.

Implementing data science initiatives to build competitive advantage is a matter of leading and not following the pack. In an industry competing for the finite market share, early-adopters of data science best practices will be the eventual winners.

“This article reposted here was originally published as the cover story in the July-2015 edition of Computer Society of India – Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July)).”

Jul

Last Sunday, the men’s singles final at Wimbledon between Djokovic and Federer was an enthralling match. Federer had hoped to become the first man to win Wimbledon a record eighth time; and Djokovic seemed to have something to prove after his recent defeat at the French Open.

The mixed doubles final, also played later that afternoon, was relatively a one-sided affair. The Paes-Hingis pair staged a clinical performance to clinch the title by decimating their opponents within 40 minutes.

During the matches, the English commentator attributed Djokovic’s win to his skill, determination, power, and accuracy, and alluded to Federer as the ’33 year old tired opponent’. Interestingly, the same commentator (or it may have been another one) ascribed Paes–Hingis’ comprehensive win to their experience, homework, and coordination.

But why did Federer really lose, despite his stellar experience of ten Wimbledon appearances and near-perfect game? And how did the ageing pair of Paes (at 42 years) and Hingis (almost at 35 years) register such a convincing victory against a much younger team? So, what does it take to succeed at the highest level in sports? And what lessons, if any, can businesses take from Wimbledon?

The Centre Court at Wimbledon provides three crucial lessons for businesses to succeed:

1. Do your homework thoroughly. Federer’s serve is the key to his game; he tends to serve deep and aggressively goes for the kill on the return of the serve. During the semi-final clash with Andy Murray, Federer’s winning points came off 5 rallies or less (on an average); whereas Murray’s winning shots came from at least 8 rallies. Federer outplayed Murray winning more than 80% of points off his first serves.

While Djokovic’s own serves were lethal, he must have studied his opponent well and had perfected his returns too. He forced Federer to play more rallies by returning his serves into areas where Federer could not attack back. The longer the rallies continued, the farther Federer had to run, and the more he became prone to making unforced errors thereby strengthening Djokovic’s chances at converting them into winners. Djokovic’s most winning points came off rallies that lasted 8 or more shots.

EXHIBIT-A shows Federer committed unusually higher number of errors and had lower serving percentages against Djokovic as compared to those against Murray.

Similarly, companies operating in the marketplace must thoroughly understand their competitors. Companies must first comprehend how their serves (like introduction of new product, feature, or category) are going to be hit back by their competitors, and then plan their strategies based on those insights. If the retaliation is weak, the market leader wins the market share; whereas if the competitor does tit for tat, then the market leader is forced to choose between carrying on the duel (with further investments) and conceding the share to the competitor. While this is true in all industries, it is most evident in oligopolistic industries like FMCG, where there usually are two dominant players. For example, similar duel happened between P&G, which introduced Crest fluoride toothpaste in 1955, and Colgate-Palmolive, which had launched the world’s first commercial toothpaste.

2. Execute well. Djokovic executed his plan [to play long precision shots] perfectly. Many of his winning shots were executed so accurately that they scraped the outsides of the baseline and the sidelines.

In the mixed doubles final, Peya and Babos showed lack of coordination early in the game by crossing each other’s paths and getting mixed-up in returning shots. On the contrary, Paes and Hingis displayed an absurdly good performance by hitting powerful returns and playing deep cross-volleys at the nets. EXHIBIT-B displays their high serving percentages (in the 80s) and zero errors, which reveals a clean and flawless execution.

In business context, perfect execution of strategies is a pre-requisite to achieving long term success. There are innumerable examples of brilliant businesses going dud due to botched executions. Kodak, despite inventing the core technology in the digital cameras, failed to execute the strategy and went bankrupt. Few other examples of companies that fell due to failed executions are Atari, Research in Motion, and Woolworths.

3. Play to Win. The two finals played at the Centre Court made this third lesson very evident. In men’s singles, Djokovic lost the second set in tie-breaker because he seemed content to passively return Federer’s serve playing from outside the baseline. He just didn’t appear to be playing to win and that cost him the set.

However, the brief rain gave him an opportunity to clear his mind and bring back his focus on winning. In the third set, there was almost a different – calmer and more focused – Djokovic playing within the baseline only to win.

Likewise in the mixed doubles, Hingis and Paes were so focused on winning that they were actually enjoying the game right from the word go. Every rally and every return was confidently played by them to win the point (and eventually the title).

This is how great businesses compete too – to win! They take bold steps and confident actions in planning and executing their strategies. They strive very hard to grasp the real needs of their customers. They go all out in devising solutions that they know will address the real needs of their customers. They leave no stone unturned to market their offerings. For example, Steve Jobs was so badly persistent on winning that he stretched himself and his team members to no measures.

Djokovic summarized this point well when he said in the post-match conference,